{"push_id":1873264319,"size":2,"distinct_size":2,"ref":"refs/heads/develop","head":"c9b7f109e736f32eb92c89aba9a551b86d8b4507","before":"8b289f1f1364adef33d2be03ff2fbfc63d15b1c8","commits":[{"sha":"983020dc5b0683a9eec51012282d038429b75fca","author":{"name":"Egil Martinsson","email":"55ca114f121e6798ec07f23d9c95500c075ad798@gmail.com"},"message":"Fixing name scope in Tensorflow Objectives","distinct":true,"url":"https://api.github.com/repos/ragulpr/wtte-rnn/commits/983020dc5b0683a9eec51012282d038429b75fca"},{"sha":"c9b7f109e736f32eb92c89aba9a551b86d8b4507","author":{"name":"Egil Martinsson","email":"55ca114f121e6798ec07f23d9c95500c075ad798@gmail.com"},"message":"Added scalefactor for alpha-output neuron. Remove `max_alpha_value `\n\n- Currently the variation given a random initialization can be very small initially especially given tanh input. This was by design to avoid exploding gradient. On the other hand it might have reverse effect. Scalefactor can be set to increase variability\nUsage:\n```\n    model.add(Lambda(wtte.output_lambda,\n                     arguments={\"init_alpha\": init_alpha,\n                                \"max_beta_value\": 4.,\n                                \"alpha_kernel_scalefactor\": 1.\n                                }))\n```\n- the experimental `max_alpha_value` which was erronusly implemented was removed. Clipping alpha is a very partial fix to avoid distribution shift to the right since f clipped beta tends to 0. TODO better solution","distinct":true,"url":"https://api.github.com/repos/ragulpr/wtte-rnn/commits/c9b7f109e736f32eb92c89aba9a551b86d8b4507"}]}
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